function score_res = sc_predict( model , feat_test)

%B * S = feat_train
%B * [S Q] = [feat_train score_train]
% B * Q = score_train
% model.Q =  inv(B'*B) * B' * score_train;

% B_test * S = feat_test
%S' * B_test' = feat_test'
S = model.S;

W = (inv(S * S') * S * feat_test')';

data_n = size( feat_test, 1);

score_res = W * model.Q + repmat( model.score_mean, data_n , 1);
